Data, Policing & the Public Interest Chief Data Office / Denis - - PowerPoint PPT Presentation

Data, Policing & the Public Interest

Chief Data Office / Denis Hamill / February 2020

Policing drivers for data management

Risk assessments allowing for early intervention before point of crisis; prevent harm; keeping communities & vulnerable people safe

Public Safety & Wellbeing Prevent & Detect Crime

Real time “intel cells”; tailored crime prevention for communities; analysis of nominals and relationships; Link crimes; Predict

• ffenders.

Accurate data ensure resources deployed efficiently; Enable efficient missing persons investigations; Trusted data entered once, accessed easily

Save Time Save Money

Reduce effort required to capture and consume data; re- usable assets; realise project savings

Stay Compliant

Compliance with GDPR, Data Protection Act, Freedom of Information, National Records of Scotland

Officer Safety Partnerships Stay Secure

Share relevant data quickly with partner agencies. Identify synergies. Health in Justice. Academic research

Minimise data breaches, and data loss; protect data Access to linked, trusted data to access situational risk and threat level Accurate data on existing services will inform new future services

Prepared for future

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Our aim is to put data at the heart of decision making to deliver a more effective and efficient policing service

“Data is an Asset” and must be managed as such

Data is now widely recognised as an asset for almost every company. The ability to quickly acquire data, process it, analyse it, to gain actionable business insight, will become a business differentiator. Like every asset, data has a lifecycle, and to manage data you must manage the data lifecycle.

Identify Acquire Store & Share Use Retire

Data Quality Data Standards Data Models Operations Social Smart Assets Channels Business Intelligence Predictive Analytics Partners External (e.g. Weather) Business value is only achieved at the stage. However, all previous steps have a cost and must be managed to ensure value can be extracted from data. (Mobile, Phone, Email) Archive Destroy Paper Single Source

• f Truth

Cyber “Data refinery”

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Partnerships

What data and where is it?

What data does Police Scotland have? - Common Business Language:

• Business understanding of what data Police Scotland holds
• Agreed business definitions compiled into glossary

Where is the data stored? - Data Mapping to Systems:

• “Common business language” mapped to data in physical

systems creating a “data map”, or data dictionary How can I find this info? - Fully discoverable from a central repository:

• This “data map” is stored in a common repository
• Anyone should be able to find/discover where our data is

Common business language Data Dictionaries

• f Key Systems

Data Model & Data Flows

Key Re-usable Artefacts:

We need to be able to answer the fundamental question of “do we know what data we have, and where it is?”

Online Data Catalog

Identify Acquire Store & Share Use Retire

Business Outcomes:

• improve usability of data
• remove ambiguities
• reduce data preparation

& reconciliation issues

• reduce project time
• data affects resource by up

to 20% (Gartner)

• foundation for compliance

Strategic Intent – Establish a central “Data Catalogue” which will be searchable by all business to ensure consistency of data definitions and system data lineage

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Police Scotland – Common Data Model

How good is your data quality?

Measure Data Quality Quantify Business Impact Identify Root Causes Remediate

To improve data quality, we must first measure it, i.e. “once we measure, we can then manage & improve”

Identify Acquire Store & Share Use Retire

Strategic Intent – Establish a Data Quality Mgt process, which will measure the quality of critical data elements and manage any Data Quality issues to resolution.

Operational Reputational Financial Compliance Data Fix DQ controls Training Enrich data

• People
• Process
• Technology
• Data

Business Outcomes Improved operations; reduced cost due to efficiency savings; improved compliance

% populated % conforming to std % valid values

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A standard approach to managing critical data

We need to identify Police Scotland’s Critical Data Elements (CDE’s), and define data standards for those CDE’s and apply those standards into our solutions. Data Standards are typically the main root cause of poor data quality.

Identify Critical Data Elements (CDE’s) Create Data Standards for each CDE

Apply Data Standards at “point of entry” and “data movement”

Enforce Data Quality controls

Data Governance

Projects BAU

Police Scotland “certified data”

• What is a Data Standard? – The desired data validation rules for CDE’s, e.g. data type, length, format, allowable

values

• What does it make easier? – Data standards applied at the point of “capture” and “movement” act as a data

control, which ensures the quality of data

Identify Acquire Store & Share Use Retire

Strategic Intent – Define data standards for all critical data elements, and ensure those standards are applied to our key authoritative source systems

Certified for Data Quality

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Delivering Data to the Business

Single Source of Truth

(e.g. Golden Nominal)

Foundation for Predictive Policing Faster, easy access to data for analytics Where do I go, to get the data I need? Where are my trusted sources of data? Where can I run my analytics from?

Identify Acquire Store & Share Use Retire

60-75% of analyst time taken by data preparations Lack of trusted nominal data restricts

• perational

processes

Strategic Intent – Establish “force-wide” Analytics Platform Strategic Intent – Establish trusted source of Nominals Strategic Intent – Establish Predictive Policing capability

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Data Literacy – “Do you speak Data?”

Data literacy is the ability to read, write and communicate data in context; deriving meaningful information from data

“teach data as a second language to enable data-driven organisation”

DATA LITERACY

Find & collect relevant data Data Location

Understand what the data represents

Data Comprehension

Understand what the data means Data Interpretation Make decisions based on data

Decision- Making

Define questions to improve practice using data

Question Posing

By 2020, 50% of organizations will lack sufficient data literacy skills Data literacy skills include the following abilities:

• Data Location – ability to find and collect relevant data
• Data Comprehension – ability to understand what the data

represents

• Data Interpretation – ability to understand what the data

means

• Decision-Making – ability to make decision to address

problems identified by data

• Question Posing – ability to define question on how to

improve practice/processes using data

Identify Acquire Store & Share Use Retire

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Emerging Focus Areas

Data Sharing Information Sharing Agreements Academic Research Health in Justice programme Scottish Government Enable partners to access data that is complete and accurate, unless there is legitimate need to withhold Data Ethics Balance between what we have the “right to do” and what is the “right thing to do” Data Ethics Steering Group Align with NPCC & Centre for Data Ethics and Innovation (CDEI) Align to National Advisory Panels & SG New Data Technologies Group Data Foundations/Quality Single common data language Data standards for critical data Trusted source of nominals Enabler for increased analytics and data sharing Enabler for single crime system

Thank You